In a method of optimizing a neural network model, first model information about a first neural network model is received. Device information about a first target device that is used to execute the first neural network model is received. An analysis whether the first neural network model is suitable for executing on the first target device is performed, based on the first model information, the device information, and at least one of a plurality of suitability determination algorithms. A result of the analysis is output such that the first model information and the result of the analysis are displayed on a screen.

Systems and methods for programming field programmable gate array (FPGA) devices are provided. A trained model for a deep learning process is obtained and converted to design abstraction (DA) code defining logic block circuits for programming an FPGA device. Each of these logic block circuits represents one of a plurality of modules that executes a processing step between different layers of the deep learning process.

A generation function to generate and output generated data from an input, a discrimination function to cause each discriminator to discriminate whether the data to be discriminated is based on the training data or the generated data and to output a discrimination result. Also an update function to update the discriminator that has output the discrimination result such that the data to be discriminated is discriminated with higher accuracy, and to further update the generator to increase a probability of discriminating that the generated data-based data to be discriminated is the training data-based data, and a whole update function to cause the updates to be executed for the generator and all the discriminators.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining an artificial neural network architecture corresponding to a sub-graph of a synaptic connectivity graph. In one aspect, there is provided a method comprising: obtaining data defining a graph representing synaptic connectivity between neurons in a brain of a biological organism; determining, for each node in the graph, a respective set of one or more node features characterizing a structure of the graph relative to the node; identifying a sub-graph of the graph, comprising selecting a proper subset of the nodes in the graph for inclusion in the sub-graph based on the node features of the nodes in the graph; and determining an artificial neural network architecture corresponding to the sub-graph of the graph.

One or more embodiments of the present disclosure include systems and methods that use neural architecture fusion to learn how to combine multiple separate pre-trained networks by fusing their architectures into a single network for better computational efficiency and higher accuracy. For example, a computer implemented method of the disclosure includes obtaining multiple trained networks. Each of the trained networks may be associated with a respective task and has a respective architecture. The method further includes generating a directed acyclic graph that represents at least a partial union of the architectures of the trained networks. The method additionally includes defining a joint objective for the directed acyclic graph that combines a performance term and a distillation term. The method also includes optimizing the joint objective over the directed acyclic graph.

Dynamic distribution of a computational graph that defines a set of operations comprising a first subset of one or more operations and a second subset of one or more operations. In one aspect, there is a method for generating output data based on the computational graph. The method includes a first device storing information related to the computational graph, the information related to the computational graph comprising information representing the first subset of operations. The method also includes the first device receiving input data and the first device performing the first subset of operations using the received input data, thereby producing first output data corresponding to the first subset of operations. The method further includes the first device exposing the first output data as a discoverable resource so that the first output data is discoverable by other devices.

A non-transitory computer-readable recording medium stores a learning program that causes a computer to execute a machine learning process for graph data. The machine learning process includes: generating, from graph data to be subjected to learning, extended graph data where at least some of nodes included in the graph data have a value of the nodes and a value corresponding to presence or absence of an indefinite element at the nodes; and obtaining input tensor data by performing tensor decomposition of the generated extended graph data, performing deep learning with a neural network by inputting the input tensor data into the neural network upon deep learning, and learning a method of the tensor decomposition.

A neural network system for predicting a polling time and a neural network model processing method using the neural network system are provided. The neural network system includes a first resource to generate a first calculation result obtained by performing at least one calculation operation corresponding to a first calculation processing graph and a task manager to calculate a first polling time taken for the first resource to perform the at least one calculation operation and to poll the first calculation result from the first resource based on the calculated first polling time.

The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.

An electronic device that includes a processor configured to execute training iterations during a training process for a neural network, each training iteration including processing a separate instance of training data through the neural network, and a sparsity monitor is described. During operation, the sparsity monitor acquires, during a monitoring interval in each of one or more monitoring periods, intermediate data output by at least some intermediate nodes of the neural network during training iterations that occur during each monitoring interval. The sparsity monitor then generates, based at least in part on the intermediate data, one or more values representing sparsity characteristics for the intermediate data. The sparsity monitor next sends, to the processor, the one or more values representing the sparsity characteristics and the processor controls one or more aspects of executing subsequent training iterations based at least in part on the values representing the sparsity characteristics.